{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dislocation_SDVPN calculation style\n",
    "\n",
    "**Lucas M. Hale**, [lucas.hale@nist.gov](mailto:lucas.hale@nist.gov?Subject=ipr-demo), *Materials Science and Engineering Division, NIST*.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The dislocation_SDVPN calculation style predicts a dislocation's planar spreading using the semidiscrete variational Peierls-Nabarro method.  The solution finds the disregistry (difference in displacement above and below the slip plane) that minimizes the dislocation's energy.  The energy term consists of two primary components: an elastic component due to the dislocation interacting with itself, and a misfit component arising from the formation of a generalized stacking fault along the dislocation's spreading.\n",
    "\n",
    "### Version notes\n",
    "\n",
    "- 2018-09-25: Notebook added\n",
    "- 2019-07-30: Notebook setup and parameters changed.\n",
    "- 2020-09-22: Notebook updated to reflect changes in the calculation method due to updates in atomman's Volterra class solution generators.  Setup and parameter definitiions streamlined.\n",
    "\n",
    "### Additional dependencies\n",
    "\n",
    "### Disclaimers\n",
    "\n",
    "- [NIST disclaimers](http://www.nist.gov/public_affairs/disclaimer.cfm)\n",
    "- The calculation method solves the problem using a 2D generalized stacking fault energy map.  Better results may be possible by measuring a full 3D map, but that would require adding a new calculation for the 3D variation.\n",
    "- The implemented method is suited for dislocations with planar spreading. It is not suited for dislocations that spread on multiple atomic planes, like the a/2<111> bcc screw dislocation.\n",
    "- While the solution is taken at discrete points that (typically) correspond to atomic sites, the underlying method is still a continuum solution that does not fully account for the atomic nature of the dislocation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method and Theory\n",
    "\n",
    "This calculation method is a wrapper around the atomman.defect.SDVPN class.  More details on the method and theory can be found in the [associated tutorial within the atomman documentation](https://www.ctcms.nist.gov/potentials/atomman/tutorial/04.4._Semidiscrete_Variational_Peierls-Nabarro.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1. Library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries needed by the calculation. The external libraries used are:\n",
    "\n",
    "- [numpy](http://www.numpy.org/)\n",
    "\n",
    "- [scipy](https://scipy.org/scipylib/)\n",
    "\n",
    "- [matplotlib](https://matplotlib.org/)\n",
    "\n",
    "- [atomman](https://github.com/usnistgov/atomman)\n",
    "\n",
    "- [iprPy](https://github.com/usnistgov/iprPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook last executed on 2020-09-22 using iprPy version 0.10.2\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "from pathlib import Path\n",
    "import os\n",
    "import datetime\n",
    "from copy import deepcopy\n",
    "\n",
    "# http://www.numpy.org/\n",
    "import numpy as np \n",
    "\n",
    "# https://matplotlib.org/\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# https://github.com/usnistgov/atomman \n",
    "import atomman as am\n",
    "import atomman.lammps as lmp\n",
    "import atomman.unitconvert as uc\n",
    "\n",
    "# https://github.com/usnistgov/iprPy\n",
    "import iprPy\n",
    "\n",
    "print('Notebook last executed on', datetime.date.today(), 'using iprPy version', iprPy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2. Default calculation setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify calculation style\n",
    "calc_style = 'dislocation_SDVPN'\n",
    "\n",
    "# If workingdir is already set, then do nothing (already in correct folder)\n",
    "try:\n",
    "    workingdir = workingdir\n",
    "\n",
    "# Change to workingdir if not already there\n",
    "except:\n",
    "    workingdir = Path('calculationfiles', calc_style)\n",
    "    if not workingdir.is_dir():\n",
    "        workingdir.mkdir(parents=True)\n",
    "    os.chdir(workingdir)\n",
    "    \n",
    "# Initialize connection to library\n",
    "with open('C:/Users/lmh1/Documents/potentials_nist_gov/password.txt') as f:\n",
    "    user, pswd = f.read().strip().split()\n",
    "library = iprPy.Library(load=['lammps_potentials'], username=user, password=pswd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Assign values for the calculation's run parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Load initial unit cell system\n",
    "\n",
    "- __ucell__ is an atomman.System representing a fundamental unit cell of the system (required).  Here, this is loaded from the database for the prototype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avect =  [ 3.520,  0.000,  0.000]\n",
      "bvect =  [ 0.000,  3.520,  0.000]\n",
      "cvect =  [ 0.000,  0.000,  3.520]\n",
      "origin = [ 0.000,  0.000,  0.000]\n",
      "natoms = 4\n",
      "natypes = 1\n",
      "symbols = ('Ni',)\n",
      "pbc = [ True  True  True]\n",
      "per-atom properties = ['atype', 'pos']\n",
      "     id |   atype |  pos[0] |  pos[1] |  pos[2]\n",
      "      0 |       1 |   0.000 |   0.000 |   0.000\n",
      "      1 |       1 |   0.000 |   1.760 |   1.760\n",
      "      2 |       1 |   1.760 |   0.000 |   1.760\n",
      "      3 |       1 |   1.760 |   1.760 |   0.000\n"
     ]
    }
   ],
   "source": [
    "# Create ucell by loading prototype record\n",
    "ucell = am.load('crystal', potential_LAMMPS_id='1999--Mishin-Y--Ni--LAMMPS--ipr1', family='A1--Cu--fcc', database=library)\n",
    "\n",
    "print(ucell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Specify material elastic constants\n",
    "\n",
    "Simple input parameters:\n",
    "\n",
    "- __C_dict__ is a dictionary containing the unique elastic constants for the potential and crystal structure defined above. \n",
    "\n",
    "Derived parameters\n",
    "\n",
    "- __C__ is an atomman.ElasticConstants object built from C_dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_dict = {}\n",
    "C_dict['C11'] = uc.set_in_units(247.86, 'GPa')\n",
    "C_dict['C12'] = uc.set_in_units(147.83, 'GPa')\n",
    "C_dict['C44'] = uc.set_in_units(124.84, 'GPa')\n",
    "\n",
    "# -------------- Derived parameters -------------- #\n",
    "# Build ElasticConstants object from C_dict terms\n",
    "C = am.ElasticConstants(**C_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Specify the defect parameters\n",
    "\n",
    "- __gammasurface_file__ gives the path to a data model file containing 2D gamma surface results.  This can be a calc_stacking_fault_map_2D record.\n",
    "\n",
    "- __burgers__ is the crystallographic Miller Burgers vector for the dislocation. \n",
    "\n",
    "- __ξ_uvw__ is the Miller \\[uvw\\] line vector direction for the dislocation.  The angle between burgers and ξ_uvw determines the dislocation's character\n",
    "\n",
    "- __slip_hkl__ is the Miller (hkl) slip plane for the dislocation.\n",
    "    \n",
    "- __m__ is the Cartesian vector of the final system that the dislocation solution's m vector (in-plane, perpendicular to ξ) should align with.  Limited to being parallel to one of the three Cartesian axes.  \n",
    "\n",
    "- __n__ is the Cartesian vector of the final system that the dislocation solution's n vector (slip plane normal) should align with.  Limited to being parallel to one of the three Cartesian axes. \n",
    "\n",
    "- __gamma__ is a GammaSurface object.  Here, it is loaded from gammasurface_file.  Note that gamma and volterra must be for the same plane.\n",
    "\n",
    "- __volterra__ is a VolterraDislocation object for the dislocation type of interest.  Here, it is created based on the elastic constants, unit cell and dislocation parameters above.  Note that gamma and volterra must be for the same plane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gammasurface_file = '../stacking_fault_map_2D/gamma.json'\n",
    "gamma = am.defect.GammaSurface(model=gammasurface_file)\n",
    "\n",
    "# fcc a/2 <110>{111} dislocations\n",
    "burgers = 0.5 * np.array([ 1., -1., 0.])\n",
    "slip_hkl = [1, 1, 1]\n",
    "\n",
    "# Line direction determines dislocation character\n",
    "ξ_uvw = [ 1, 1,-2] # 90 degree edge\n",
    "#ξ_uvw = [ 1, 0,-1] # 60 degree mixed\n",
    "#ξ_uvw = [ 1,-2, 1] # 30 degree mixed\n",
    "#ξ_uvw = [ 1,-1, 0] # 0 degree screw\n",
    "\n",
    "# Best choice for m + n as it works for non-cubic systems\n",
    "m = [0,1,0]\n",
    "n = [0,0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Specify calculation-specific run parameters\n",
    "\n",
    "- __xmax__: The maximum value of the x-coordinates to use for the points where the disregistry is evaluated.  The solution is centered around x=0, therefore this also corresponds to the minimum value of x used.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __xstep__: The step size (delta x) value between the x-coordinates used to evaluate the disregistry.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __xnum__: The total number of x-coordinates at which to evaluate the disregistry.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __min_method__: The scipy.optimize.minimize method style to use when solving for the disregistry.  Default value is 'Powell', which seems to do decently well for this problem.\n",
    "\n",
    "- __min_options__: Allows for the specification of the options dictionary used by scipy.optimize.minimize. This is given as \"key value key value...\".\n",
    "\n",
    "- __min_cycles__: Specifies the number of times to run the minimization in succession.  The minimization algorithms used by the underlying scipy code often benefit from restarting and rerunning the minimized configuration to achive a better fit.  Default value is 10.\n",
    "\n",
    "- __cutofflongrange__: The radial cutoff (in distance units) to use for the long-range elastic energy.  The long-range elastic energy is configuration-independent, so this value changes the dislocation's energy but not the computed disregistry profile. Default value is 1000 Angstroms.\n",
    "\n",
    "- __tau_xy__: Shear stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __tau_yy__: Normal stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __tau_yz__: Shear stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __alpha__: Coefficient(s) (in pressure/length units) of the non-local energy correction term to use.  Default value is 0.0, meaning this correction is not applied.\n",
    "\n",
    "- __beta_xx, beta_yy, beta_zz, beta_xy, beta_xz, beta_yz__: Components of the surface energy coefficient tensor (in units pressure-length) to use. Default value is 0.0 GPa-Angstrom for all, meaning this correction is not applied.\n",
    "\n",
    "- __cdiffelastic, cdiffsurface, cdiffstress__: Booleans indicating how the dislocation density (derivative of disregistry) is computed within the elastic, surface and stress terms, respectively. If True, central difference is used, otherwise only the change between the current and previous points is used. Default values are True for cdiffsurface, and False for the other two.\n",
    "\n",
    "- __halfwidth__: The arctan disregistry halfwidth (in length units) to use for creating the initial disregistry guess.\n",
    "\n",
    "- __normalizedisreg__: Boolean indicating how the disregistry profile is handled.  If True (default), the disregistry is scaled such that the minimum x value has a disregistry of 0 and the maximum x value has a disregistry equal to the dislocation's Burgers vector.  Note that the disregistry for these endpoints is fixed, so if you use False the initial disregistry should be close to the final solution.\n",
    "\n",
    "- __fullstress__: Boolean indicating which of two stress formulas to use.  True uses the original full formulation, while False uses a newer, simpler representation.  Default value is True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmax = None\n",
    "xstep = 1/5\n",
    "xnum = 200\n",
    "xscale = True\n",
    "\n",
    "min_method = 'Powell'\n",
    "min_options = {}\n",
    "min_options['disp'] = True # will display convergence info\n",
    "min_options['xtol'] = 1e-6 # smaller convergence tolerance than default\n",
    "min_options['ftol'] = 1e-6 # smaller convergence tolerance than default\n",
    "#min_options['maxiter'] = 2 # for testing purposes\n",
    "min_cycles = 10\n",
    "\n",
    "cutofflongrange = uc.set_in_units(1000, 'angstrom')\n",
    "halfwidth = uc.set_in_units(5, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define calculation function(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. peierlsnabarro()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def peierlsnabarro(ucell, C, burgers, ξ_uvw, slip_hkl, gamma,\n",
    "                   m=[0,1,0], n=[0,0,1],\n",
    "                   cutofflongrange=uc.set_in_units(1000, 'angstrom'),\n",
    "                   tau=np.zeros((3,3)), alpha=[0.0], beta=np.zeros((3,3)),\n",
    "                   cdiffelastic=False, cdiffsurface=True, cdiffstress=False,\n",
    "                   fullstress=True,\n",
    "                   halfwidth=uc.set_in_units(1, 'angstrom'),\n",
    "                   normalizedisreg=True,\n",
    "                   xnum=None, xmax=None, xstep=None, xscale=False,\n",
    "                   min_method='Powell', min_options={}, min_cycles=10):\n",
    "    \"\"\"\n",
    "    Solves a Peierls-Nabarro dislocation model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ucell : atomman.System\n",
    "        The unit cell to use as the seed for the dislocation system.  Note that\n",
    "        only box information is used and not atomic positions.\n",
    "    C : atomman.ElasticConstants\n",
    "        The elastic constants associated with the bulk crystal structure\n",
    "        for ucell.\n",
    "    burgers : array-like object\n",
    "        The dislocation's Burgers vector given as a Miller or\n",
    "        Miller-Bravais vector relative to ucell.\n",
    "    ξ_uvw : array-like object\n",
    "        The dislocation's line direction given as a Miller or\n",
    "        Miller-Bravais vector relative to ucell.\n",
    "    slip_hkl : array-like object\n",
    "        The dislocation's slip plane given as a Miller or Miller-Bravais\n",
    "        plane relative to ucell.\n",
    "    m : array-like object, optional\n",
    "        The m unit vector for the dislocation solution.  m, n, and ξ\n",
    "        (dislocation line) should be right-hand orthogonal.  Default value\n",
    "        is [0,1,0] (y-axis).\n",
    "    n : array-like object, optional\n",
    "        The n unit vector for the dislocation solution.  m, n, and ξ\n",
    "        (dislocation line) should be right-hand orthogonal.  Default value\n",
    "        is [0,0,1] (z-axis). n is normal to the dislocation slip plane.\n",
    "    cutofflongrange : float, optional\n",
    "        The cutoff distance to use for computing the long-range energy.\n",
    "        Default value is 1000 angstroms.\n",
    "    tau : numpy.ndarray, optional\n",
    "        A (3,3) array giving the stress tensor to apply to the system\n",
    "        using the stress energy term.  Only the xy, yy, and yz components\n",
    "        are used.  Default value is all zeros.\n",
    "    alpha : list of float, optional\n",
    "        The alpha coefficient(s) used by the nonlocal energy term.  Default\n",
    "        value is [0.0].\n",
    "    beta : numpy.ndarray, optional\n",
    "        The (3,3) array of beta coefficient(s) used by the surface energy\n",
    "        term.  Default value is all zeros.\n",
    "    cdiffelastic : bool, optional\n",
    "        Flag indicating if the dislocation density for the elastic energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Default value is False.\n",
    "    cdiffsurface : bool, optional\n",
    "        Flag indicating if the dislocation density for the surface energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Default value is True.\n",
    "    cdiffstress : bool, optional\n",
    "        Flag indicating if the dislocation density for the stress energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Only matters if fullstress is True.\n",
    "        Default value is False.\n",
    "    fullstress : bool, optional\n",
    "        Flag indicating which stress energy algorithm to use.  Default\n",
    "        value is True.\n",
    "    halfwidth : float, optional\n",
    "        A dislocation halfwidth guess to use for generating the initial\n",
    "        disregistry guess.  Does not have to be accurate, but the better the\n",
    "        guess the fewer minimization steps will likely be needed.  Default\n",
    "        value is 1 Angstrom.\n",
    "    normalizedisreg : bool, optional\n",
    "        If True, the initial disregistry guess will be scaled such that it\n",
    "        will have a value of 0 at the minimum x and a value of burgers at the\n",
    "        maximum x.  Default value is True.  Note: the disregistry of end points\n",
    "        are fixed, thus True is usually preferential.\n",
    "    xnum :  int, optional\n",
    "        The number of x value points to use for the solution.  Two of xnum,\n",
    "        xmax, and xstep must be given.\n",
    "    xmax : float, optional\n",
    "        The maximum value of x to use.  Note that the minimum x value will be\n",
    "        -xmax, thus the range of x will be twice xmax.  Two of xnum, xmax, and\n",
    "        xstep must be given.\n",
    "    xstep : float, optional\n",
    "        The delta x value to use, i.e. the step size between the x values used.\n",
    "        Two of xnum, xmax, and xstep must be given.\n",
    "    xscale : bool, optional\n",
    "        Flag indicating if xmax and/or xstep values are to be taken as absolute\n",
    "        or relative to ucell's a lattice parameter.  Default value is False,\n",
    "        i.e. the x parameters are absolute and not scaled.\n",
    "    min_method : str, optional\n",
    "        The scipy.optimize.minimize method to use.  Default value is\n",
    "        'Powell'.\n",
    "    min_options : dict, optional\n",
    "        Any options to pass on to scipy.optimize.minimize. Default value\n",
    "        is {}.\n",
    "    min_cycles : int, optional\n",
    "        The number of minimization runs to perform on the system.  Restarting\n",
    "        after obtaining a solution can help further refine to the best pathway.\n",
    "        Default value is 10. \n",
    "    \"\"\"\n",
    "\n",
    "    # Solve Volterra dislocation\n",
    "    volterra = am.defect.solve_volterra_dislocation(C, burgers, ξ_uvw=ξ_uvw,\n",
    "                                                    slip_hkl=slip_hkl, box=ucell.box,\n",
    "                                                    m=m, n=n)\n",
    "    \n",
    "    # Generate SDVPN object\n",
    "    pnsolution = am.defect.SDVPN(volterra=volterra, gamma=gamma,\n",
    "                                 tau=tau, alpha=alpha, beta=beta,\n",
    "                                 cutofflongrange=cutofflongrange,\n",
    "                                 fullstress=fullstress, cdiffelastic=cdiffelastic,\n",
    "                                 cdiffsurface=cdiffsurface, cdiffstress=cdiffstress,\n",
    "                                 min_method=min_method, min_options=min_options)\n",
    "\n",
    "    # Scale xmax and xstep by alat\n",
    "    if xscale is True:\n",
    "        if xmax is not None:\n",
    "            xmax *= ucell.box.a\n",
    "        if xstep is not None:\n",
    "            xstep *= ucell.box.a\n",
    "    \n",
    "    # Generate initial disregistry guess\n",
    "    x, idisreg = am.defect.pn_arctan_disregistry(xmax=xmax, xstep=xstep, xnum=xnum,\n",
    "                                                 burgers=pnsolution.burgers,\n",
    "                                                 halfwidth=halfwidth,\n",
    "                                                 normalize=normalizedisreg)\n",
    "    \n",
    "    # Set up loop parameters\n",
    "    cycle = 0\n",
    "    disregistries = [idisreg]\n",
    "    minimization_energies = [pnsolution.total_energy(x, idisreg)]\n",
    "    \n",
    "    # Run minimization for min_cycles\n",
    "    pnsolution.x = x\n",
    "    pnsolution.disregistry = idisreg\n",
    "    while cycle < min_cycles:\n",
    "        cycle += 1\n",
    "        pnsolution.solve()\n",
    "        disregistries.append(pnsolution.disregistry)\n",
    "        minimization_energies.append(pnsolution.total_energy())\n",
    "\n",
    "    # Initialize results dict\n",
    "    results_dict = {}\n",
    "    results_dict['SDVPN_solution'] = pnsolution\n",
    "    results_dict['minimization_energies'] = minimization_energies\n",
    "    results_dict['disregistry_profiles'] = disregistries\n",
    "    \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Run calculation function(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905269\n",
      "         Iterations: 25\n",
      "         Function evaluations: 170079\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 2\n",
      "         Function evaluations: 16436\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8635\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8857\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8994\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 9031\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8931\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 9163\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8961\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8952\n"
     ]
    }
   ],
   "source": [
    "results_dict = peierlsnabarro(ucell, C, burgers, ξ_uvw, slip_hkl, gamma,\n",
    "                              m = m,\n",
    "                              n = n,\n",
    "                              cutofflongrange = cutofflongrange,\n",
    "                              #tau = tau,    \n",
    "                              #alpha = alpha,\n",
    "                              #beta = beta,\n",
    "                              #cdiffelastic = cdiffelastic,\n",
    "                              #cdiffsurface = cdiffsurface,\n",
    "                              #cdiffstress = cdiffstress,\n",
    "                              #fullstress = fullstress,\n",
    "                              halfwidth = halfwidth,\n",
    "                              #normalizedisreg = normalizedisreg,\n",
    "                              xnum = xnum,\n",
    "                              xstep = xstep,\n",
    "                              xmax = xmax,\n",
    "                              xscale = xscale,\n",
    "                              min_method = min_method,\n",
    "                              min_options = min_options,\n",
    "                              min_cycles = min_cycles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['SDVPN_solution', 'minimization_energies', 'disregistry_profiles'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Report results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Define units for outputting values\n",
    "\n",
    "- __length_unit__ is the unit of length to display results in.\n",
    "- __energy_per_length_unit__ is the unit of energy per length to display dislocation energies in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_unit = 'angstrom'\n",
    "energyperlength_unit = 'eV/angstrom'\n",
    "energyperarea_unit = 'mJ/m^2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Solution\n",
    "\n",
    "SDVPN_solution in the results dictionary is an am.Defect.SDVPN object which contains the final disregistry information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].disregistry_plot(length_unit=length_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].E_gsf_surface_plot(length_unit=length_unit, energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].E_gsf_vs_x_plot(length_unit=length_unit, energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 Minimum energy per cycle\n",
    "\n",
    "minimization_energies in the results dict lists the total computed dislocation energy for the initial disregistry and after each minimization run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dislocation energy per minimization run (in eV/angstrom):\n",
      "0 5.344452611702839\n",
      "1 4.905268833729996\n",
      "2 4.90526343054055\n",
      "3 4.90526328741267\n",
      "4 4.905263191449777\n",
      "5 4.905263106389016\n",
      "6 4.9052630221379605\n",
      "7 4.9052629360389375\n",
      "8 4.9052628475353695\n",
      "9 4.905262756612204\n",
      "10 4.905262663339871\n"
     ]
    }
   ],
   "source": [
    "print('Total dislocation energy per minimization run (in %s):' % energyperlength_unit)\n",
    "for i, E_total in enumerate(results_dict['minimization_energies']):\n",
    "    print(i, uc.get_in_units(E_total, energyperlength_unit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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